毫米波雷达用于坐立分析：与可穿戴设备和Kinect的对比研究
mmWave Radar for Sit-to-Stand Analysis: A Comparative Study with Wearables and Kinect
AI总结：
本研究探索了用60GHz毫米波雷达分析坐立动作的新方法，通过收集45名参与者的点云数据，结合深度学习姿态估计与逆运动学提取特征并与可穿戴设备、Kinect对比，证明了其有效性并提出集成传感器技术的建议。
摘要：
本研究探索了一种利用毫米波（mmWave）雷达技术分析坐立（STS）动作的新方法，旨在开发一种非接触式传感、保护隐私且可全天运行的医疗应用方法，包括跌倒风险评估。我们使用60GHz毫米波雷达系统收集雷达点云数据，捕捉45名参与者的STS动作；通过深度学习姿态估计模型从Kinect内置人体跟踪中学习人体骨骼，应用逆运动学（IK）计算关节角度、分割STS动作并提取跌倒风险评估常用特征；随后将雷达提取的特征与Kinect和可穿戴传感器获取的特征进行对比。结果表明毫米波雷达能有效捕捉整体运动模式和大关节动作（如躯干），还指出了各传感器的优缺点，建议集成传感器技术以提高临床和生物医学研究中运动分析的准确性和可靠性。
英文摘要：
This study explores a novel approach for analyzing Sit-to-Stand (STS) movements using millimeter-wave (mmWave) radar technology. The goal is to develop a non-contact sensing, privacy-preserving, and all-day operational method for healthcare applications, including fall risk assessment. We used a 60GHz mmWave radar system to collect radar point cloud data, capturing STS motions from 45 participants. By employing a deep learning pose estimation model, we learned the human skeleton from Kinect built-in body tracking and applied Inverse Kinematics (IK) to calculate joint angles, segment STS motions, and extract commonly used features in fall risk assessment. Radar extracted features were then compared with those obtained from Kinect and wearable sensors. The results demonstrated the effectiveness of mmWave radar in capturing general motion patterns and large joint movements (e.g., trunk). Additionally, the study highlights the advantages and disadvantages of individual sensors and suggests the potential of integrated sensor technologies to improve the accuracy and reliability of motion analysis in clinical and biomedical research settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
